Funded by The Australian Research Council (Discovery Project)

Funding Amount: $353,000

Project start date: 2023

Researchers

  • A/Prof Yanrong Yang (CI), ANU
  • Prof Hanlin Shang (CI), Macquarie University
  • Prof Degui Li (PI), Chinese University of Hong Kong
  • Dr Xinghao Qiao (PI), London School of Economics and Political Science
  • Asst Prof Qingliang Fan (PI), The University of York

Project Description

This project aims to develop new methods and theories for common features on high-dimensional functional time series observed in empirical applications. The significance includes addressing a key gap in adaptive and efficient feature learning, improving forecasting accuracy and understanding forecasting-driven factors comprehensively for empirical data. Expected outcomes involve advances in big data theory and easy-to-implement algorithms for applied researchers. This project benefits not only advanced manufacturing by finding optimal stopping time for wood panel compression, but also superior forecasting for mortality in demography, climate data in environmental science, asset returns in finance, and electricity consumption in economics.

National Interest Statement

Australia’s life insurance, superannuation and pension funds industries carry significant responsibility for the financial wellbeing of Australians. Managing this responsibility and financial risk depends on accurately pricing consumers’ insurance premiums. To set those premiums, the industry analyses data to make predictions about individual mortality, yet technological advances have produced unprecedented volumes and sources of possible data to choose from and merge. This makes life expectancy forecasting and premium-setting potentially inaccurate. This project will develop new theories, methodologies and algorithms that account for complexities in merged big datasets to improve the accuracy of predictions. Translated into a purpose-built open access software program coupled with industry practitioner training, our research will build industry’s capacity to use these new methodologies leading to improvements in mortality forecasts and pricing of life insurance premiums for everyday Australians, as well as stronger financial risk management among some of Australia’s most critical financial industries.